A novel workflow for the inverse QSPR problem using multiobjective optimization.

A workflow for the inverse quantitative structure-property relationship (QSPR) problem is reported in this paper for the de novo design of novel chemical entities (NCE) in silico through the application of existing QSPR models to calculate multiple objectives, including prediction confidence measures, to be optimized during the de novo design process. Two physical property datasets are applied as case studies of the inverse QSPR workflow (IQW): mean molecular polarizability and aqueous solubility. The case studies demonstrate the optimization of molecular structures to within a property range of interest; the optimized structures are then validated against QSPR models that are generated from sets of alternative descriptors to those used in the IQW. The paper concludes with a discussion of the results from the case studies.

A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules.

In this paper we propose a novel graph-based genetic algorithm for the evolution of novel molecular graphs from a predefined set of elements or molecular fragments with an external objective function. A brief overview of existing genetic algorithm approaches in molecular design is provided followed by a description of our approach. The paper continues to suggest a novel application of this program to the multiobjective evolution of median molecules that are structurally representative of a set of objective molecules. We conclude with a summary of our initial results along with a discussion of a variety of improvements and applications of our approach.

The de novo design of median molecules within a property range of interest.

In this paper an application is presented of the median molecule workflow to the de novo design of novel molecular entities with a property profile of interest. Median molecules are structures that are optimised to be similar to a set of existing molecules of interest as an approach for lead exploration and hopping. An overview of this workflow is provided together with an example of an instance using the similarity to camphor and menthol as objectives. The methodology of the experiments is defined and the workflow is applied to designing novel molecules for two physical property datasets: mean molecular polarisability and aqueous solubility. This paper concludes with a discussion of the characteristics of this method.

Advances in multivariate analysis in pharmaceutical process development.

In the last five years, reports of the application of multivariate methods in pharmaceutical process research and development have burgeoned. Examples range from the widespread adoption of statistical experimental design for screening and process optimization to the implementation of workflows that integrate multivariate characterization, experimental design and the development of quantitative structure-property relationships. Having learned from the application of these techniques in drug discovery and armed with modern high-throughput experimentation platforms, practitioners are discovering the power of experimental design and multivariate methods for shortening process development timelines.